Abstract

The discrimination of engineered nanoparticles (ENPs) from the natural geogenic background is one of the preeminent challenges for assessing their potential implications. At low ENP concentrations, most conventional analytical techniques are not able to take advantage of inherent differences (e.g. in terms of composition, isotopic signatures, element ratios, structure, shape or surface characteristics) between ENPs and naturally occurring nanoscale particles (NNPs) of similar composition. Here, we present a groundbreaking approach to overcome these limitations and enable the discrimination of man-made ENPs from NNPs through simultaneous detection of multiple elements on an individual particle level. This new analytical approach is accessible by an inductively-coupled plasma time-of-flight mass spectrometer (ICP-TOFMS) operated in single-particle mode. Machine learning is employed to classify ENPs and NNPs based on their unique elemental fingerprints and quantify their concentrations. We demonstrate the applicability of this single-particle multi-element fingerprinting (spMEF) method by distinguishing engineered cerium oxide nanoparticles (CeO2 ENPs) from natural Ce-containing nanoparticles (Ce-NNPs) in soils at environmentally relevant ENP concentrations, orders of magnitude below the natural background.

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